Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations6362620
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory534.0 MiB
Average record size in memory88.0 B

Variable types

Numeric6
Categorical3
Text2

Alerts

amount is highly overall correlated with newbalanceDest and 1 other fieldsHigh correlation
newbalanceDest is highly overall correlated with amount and 1 other fieldsHigh correlation
newbalanceOrig is highly overall correlated with oldbalanceOrgHigh correlation
oldbalanceDest is highly overall correlated with amount and 1 other fieldsHigh correlation
oldbalanceOrg is highly overall correlated with newbalanceOrigHigh correlation
isFraud is highly imbalanced (98.6%) Imbalance
isFlaggedFraud is highly imbalanced (> 99.9%) Imbalance
amount is highly skewed (γ1 = 30.99394948) Skewed
oldbalanceOrg has 2102449 (33.0%) zeros Zeros
newbalanceOrig has 3609566 (56.7%) zeros Zeros
oldbalanceDest has 2704388 (42.5%) zeros Zeros
newbalanceDest has 2439433 (38.3%) zeros Zeros

Reproduction

Analysis started2025-08-31 11:23:19.203504
Analysis finished2025-08-31 11:29:03.171349
Duration5 minutes and 43.97 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

step
Real number (ℝ)

Distinct743
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.39725
Minimum1
Maximum743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.5 MiB
2025-08-31T11:29:03.273661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q1156
median239
Q3335
95-th percentile490
Maximum743
Range742
Interquartile range (IQR)179

Descriptive statistics

Standard deviation142.33197
Coefficient of variation (CV)0.58477232
Kurtosis0.32907056
Mean243.39725
Median Absolute Deviation (MAD)92
Skewness0.37517689
Sum1.5486442 × 109
Variance20258.39
MonotonicityIncreasing
2025-08-31T11:29:03.420056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 51352
 
0.8%
18 49579
 
0.8%
187 49083
 
0.8%
235 47491
 
0.7%
307 46968
 
0.7%
163 46352
 
0.7%
139 46054
 
0.7%
403 45155
 
0.7%
43 45060
 
0.7%
355 44787
 
0.7%
Other values (733) 5890739
92.6%
ValueCountFrequency (%)
1 2708
 
< 0.1%
2 1014
 
< 0.1%
3 552
 
< 0.1%
4 565
 
< 0.1%
5 665
 
< 0.1%
6 1660
 
< 0.1%
7 6837
 
0.1%
8 21097
0.3%
9 37628
0.6%
10 35991
0.6%
ValueCountFrequency (%)
743 8
 
< 0.1%
742 14
< 0.1%
741 22
< 0.1%
740 6
 
< 0.1%
739 10
< 0.1%
738 10
< 0.1%
737 10
< 0.1%
736 14
< 0.1%
735 12
< 0.1%
734 8
 
< 0.1%

type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.5 MiB
CASH_OUT
2237500 
PAYMENT
2151495 
CASH_IN
1399284 
TRANSFER
532909 
DEBIT
 
41432

Length

Max length8
Median length7
Mean length7.422396
Min length5

Characters and Unicode

Total characters47225885
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAYMENT
2nd rowPAYMENT
3rd rowTRANSFER
4th rowCASH_OUT
5th rowPAYMENT

Common Values

ValueCountFrequency (%)
CASH_OUT 2237500
35.2%
PAYMENT 2151495
33.8%
CASH_IN 1399284
22.0%
TRANSFER 532909
 
8.4%
DEBIT 41432
 
0.7%

Length

2025-08-31T11:29:03.569010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-31T11:29:03.654755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cash_out 2237500
35.2%
payment 2151495
33.8%
cash_in 1399284
22.0%
transfer 532909
 
8.4%
debit 41432
 
0.7%

Most occurring characters

ValueCountFrequency (%)
A 6321188
13.4%
T 4963336
10.5%
S 4169693
8.8%
N 4083688
8.6%
C 3636784
 
7.7%
_ 3636784
 
7.7%
H 3636784
 
7.7%
E 2725836
 
5.8%
O 2237500
 
4.7%
U 2237500
 
4.7%
Other values (8) 9576792
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47225885
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 6321188
13.4%
T 4963336
10.5%
S 4169693
8.8%
N 4083688
8.6%
C 3636784
 
7.7%
_ 3636784
 
7.7%
H 3636784
 
7.7%
E 2725836
 
5.8%
O 2237500
 
4.7%
U 2237500
 
4.7%
Other values (8) 9576792
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47225885
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 6321188
13.4%
T 4963336
10.5%
S 4169693
8.8%
N 4083688
8.6%
C 3636784
 
7.7%
_ 3636784
 
7.7%
H 3636784
 
7.7%
E 2725836
 
5.8%
O 2237500
 
4.7%
U 2237500
 
4.7%
Other values (8) 9576792
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47225885
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 6321188
13.4%
T 4963336
10.5%
S 4169693
8.8%
N 4083688
8.6%
C 3636784
 
7.7%
_ 3636784
 
7.7%
H 3636784
 
7.7%
E 2725836
 
5.8%
O 2237500
 
4.7%
U 2237500
 
4.7%
Other values (8) 9576792
20.3%

amount
Real number (ℝ)

High correlation  Skewed 

Distinct5316900
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179861.9
Minimum0
Maximum92445517
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size48.5 MiB
2025-08-31T11:29:03.802156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2224.0995
Q113389.57
median74871.94
Q3208721.48
95-th percentile518634.2
Maximum92445517
Range92445517
Interquartile range (IQR)195331.91

Descriptive statistics

Standard deviation603858.23
Coefficient of variation (CV)3.3573437
Kurtosis1797.9567
Mean179861.9
Median Absolute Deviation (MAD)68393.655
Skewness30.993949
Sum1.1443929 × 1012
Variance3.6464476 × 1011
MonotonicityNot monotonic
2025-08-31T11:29:03.937075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000000 3207
 
0.1%
10000 88
 
< 0.1%
5000 79
 
< 0.1%
15000 68
 
< 0.1%
500 65
 
< 0.1%
100000 42
 
< 0.1%
21500 37
 
< 0.1%
120000 29
 
< 0.1%
135000 20
 
< 0.1%
0 16
 
< 0.1%
Other values (5316890) 6358969
99.9%
ValueCountFrequency (%)
0 16
< 0.1%
0.01 1
 
< 0.1%
0.02 3
 
< 0.1%
0.03 2
 
< 0.1%
0.04 1
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 1
 
< 0.1%
0.11 2
 
< 0.1%
ValueCountFrequency (%)
92445516.64 1
< 0.1%
73823490.36 1
< 0.1%
71172480.42 1
< 0.1%
69886731.3 1
< 0.1%
69337316.27 1
< 0.1%
67500761.29 1
< 0.1%
66761272.21 1
< 0.1%
64234448.19 1
< 0.1%
63847992.58 1
< 0.1%
63294839.63 1
< 0.1%
Distinct6353307
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size48.5 MiB
2025-08-31T11:29:10.774782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.482323
Min length5

Characters and Unicode

Total characters66695040
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6344009 ?
Unique (%)99.7%

Sample

1st rowC1231006815
2nd rowC1666544295
3rd rowC1305486145
4th rowC840083671
5th rowC2048537720
ValueCountFrequency (%)
c1677795071 3
 
< 0.1%
c724452879 3
 
< 0.1%
c1462946854 3
 
< 0.1%
c1784010646 3
 
< 0.1%
c1902386530 3
 
< 0.1%
c1976208114 3
 
< 0.1%
c363736674 3
 
< 0.1%
c2051359467 3
 
< 0.1%
c545315117 3
 
< 0.1%
c1065307291 3
 
< 0.1%
Other values (6353297) 6362590
> 99.9%
2025-08-31T11:29:17.042715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8803448
13.2%
C 6362620
9.5%
2 6136135
9.2%
3 5699596
8.5%
4 5693146
8.5%
7 5669437
8.5%
5 5668010
8.5%
6 5667725
8.5%
0 5667074
8.5%
9 5665212
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66695040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8803448
13.2%
C 6362620
9.5%
2 6136135
9.2%
3 5699596
8.5%
4 5693146
8.5%
7 5669437
8.5%
5 5668010
8.5%
6 5667725
8.5%
0 5667074
8.5%
9 5665212
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66695040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8803448
13.2%
C 6362620
9.5%
2 6136135
9.2%
3 5699596
8.5%
4 5693146
8.5%
7 5669437
8.5%
5 5668010
8.5%
6 5667725
8.5%
0 5667074
8.5%
9 5665212
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66695040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8803448
13.2%
C 6362620
9.5%
2 6136135
9.2%
3 5699596
8.5%
4 5693146
8.5%
7 5669437
8.5%
5 5668010
8.5%
6 5667725
8.5%
0 5667074
8.5%
9 5665212
8.5%

oldbalanceOrg
Real number (ℝ)

High correlation  Zeros 

Distinct1845844
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean833883.1
Minimum0
Maximum59585040
Zeros2102449
Zeros (%)33.0%
Negative0
Negative (%)0.0%
Memory size48.5 MiB
2025-08-31T11:29:17.201804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14208
Q3107315.18
95-th percentile5823702.3
Maximum59585040
Range59585040
Interquartile range (IQR)107315.18

Descriptive statistics

Standard deviation2888242.7
Coefficient of variation (CV)3.4636062
Kurtosis32.964879
Mean833883.1
Median Absolute Deviation (MAD)14208
Skewness5.2491364
Sum5.3056813 × 1012
Variance8.3419457 × 1012
MonotonicityNot monotonic
2025-08-31T11:29:17.343020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2102449
33.0%
184 918
 
< 0.1%
133 914
 
< 0.1%
195 912
 
< 0.1%
164 909
 
< 0.1%
109 908
 
< 0.1%
181 908
 
< 0.1%
157 902
 
< 0.1%
146 899
 
< 0.1%
128 898
 
< 0.1%
Other values (1845834) 4252003
66.8%
ValueCountFrequency (%)
0 2102449
33.0%
0.05 1
 
< 0.1%
0.18 1
 
< 0.1%
0.21 1
 
< 0.1%
0.44 1
 
< 0.1%
0.67 1
 
< 0.1%
1 370
 
< 0.1%
1.02 1
 
< 0.1%
1.37 1
 
< 0.1%
1.38 1
 
< 0.1%
ValueCountFrequency (%)
59585040.37 1
< 0.1%
57316255.05 1
< 0.1%
50399045.08 1
< 0.1%
49585040.37 1
< 0.1%
47316255.05 1
< 0.1%
45674547.89 1
< 0.1%
44892193.09 1
< 0.1%
43818855.3 1
< 0.1%
43686616.33 1
< 0.1%
42542664.27 1
< 0.1%

newbalanceOrig
Real number (ℝ)

High correlation  Zeros 

Distinct2682586
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean855113.67
Minimum0
Maximum49585040
Zeros3609566
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size48.5 MiB
2025-08-31T11:29:17.488033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3144258.41
95-th percentile5980262.3
Maximum49585040
Range49585040
Interquartile range (IQR)144258.41

Descriptive statistics

Standard deviation2924048.5
Coefficient of variation (CV)3.4194852
Kurtosis32.066985
Mean855113.67
Median Absolute Deviation (MAD)0
Skewness5.176884
Sum5.4407633 × 1012
Variance8.5500596 × 1012
MonotonicityNot monotonic
2025-08-31T11:29:17.629025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3609566
56.7%
7468.59 4
 
< 0.1%
45633.24 4
 
< 0.1%
7802.01 4
 
< 0.1%
16828.17 4
 
< 0.1%
31285.61 4
 
< 0.1%
32926.52 4
 
< 0.1%
7300.24 4
 
< 0.1%
9018.87 4
 
< 0.1%
9897.82 4
 
< 0.1%
Other values (2682576) 2753018
43.3%
ValueCountFrequency (%)
0 3609566
56.7%
0.01 1
 
< 0.1%
0.03 1
 
< 0.1%
0.05 1
 
< 0.1%
0.12 1
 
< 0.1%
0.13 1
 
< 0.1%
0.18 1
 
< 0.1%
0.21 1
 
< 0.1%
0.23 1
 
< 0.1%
0.3 1
 
< 0.1%
ValueCountFrequency (%)
49585040.37 1
< 0.1%
47316255.05 1
< 0.1%
43686616.33 1
< 0.1%
43673802.21 1
< 0.1%
41690842.64 1
< 0.1%
41432359.46 1
< 0.1%
40399045.08 1
< 0.1%
39585040.37 1
< 0.1%
38946233.02 1
< 0.1%
38939424.03 1
< 0.1%
Distinct2722362
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Memory size48.5 MiB
2025-08-31T11:29:19.975533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.481752
Min length2

Characters and Unicode

Total characters66691405
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2262704 ?
Unique (%)35.6%

Sample

1st rowM1979787155
2nd rowM2044282225
3rd rowC553264065
4th rowC38997010
5th rowM1230701703
ValueCountFrequency (%)
c1286084959 113
 
< 0.1%
c985934102 109
 
< 0.1%
c665576141 105
 
< 0.1%
c2083562754 102
 
< 0.1%
c1590550415 101
 
< 0.1%
c248609774 101
 
< 0.1%
c1789550256 99
 
< 0.1%
c451111351 99
 
< 0.1%
c1360767589 98
 
< 0.1%
c1023714065 97
 
< 0.1%
Other values (2722352) 6361596
> 99.9%
2025-08-31T11:29:22.939904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8799996
13.2%
2 6133780
9.2%
3 5704404
8.6%
4 5691070
8.5%
8 5675627
8.5%
9 5668861
8.5%
7 5665128
8.5%
0 5664751
8.5%
6 5662897
8.5%
5 5662271
8.5%
Other values (2) 6362620
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66691405
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8799996
13.2%
2 6133780
9.2%
3 5704404
8.6%
4 5691070
8.5%
8 5675627
8.5%
9 5668861
8.5%
7 5665128
8.5%
0 5664751
8.5%
6 5662897
8.5%
5 5662271
8.5%
Other values (2) 6362620
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66691405
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8799996
13.2%
2 6133780
9.2%
3 5704404
8.6%
4 5691070
8.5%
8 5675627
8.5%
9 5668861
8.5%
7 5665128
8.5%
0 5664751
8.5%
6 5662897
8.5%
5 5662271
8.5%
Other values (2) 6362620
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66691405
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8799996
13.2%
2 6133780
9.2%
3 5704404
8.6%
4 5691070
8.5%
8 5675627
8.5%
9 5668861
8.5%
7 5665128
8.5%
0 5664751
8.5%
6 5662897
8.5%
5 5662271
8.5%
Other values (2) 6362620
9.5%

oldbalanceDest
Real number (ℝ)

High correlation  Zeros 

Distinct3614697
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1100701.7
Minimum0
Maximum3.5601589 × 108
Zeros2704388
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size48.5 MiB
2025-08-31T11:29:23.163242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median132705.66
Q3943036.71
95-th percentile5147229.7
Maximum3.5601589 × 108
Range3.5601589 × 108
Interquartile range (IQR)943036.71

Descriptive statistics

Standard deviation3399180.1
Coefficient of variation (CV)3.0881938
Kurtosis948.67413
Mean1100701.7
Median Absolute Deviation (MAD)132705.66
Skewness19.921758
Sum7.0033464 × 1012
Variance1.1554425 × 1013
MonotonicityNot monotonic
2025-08-31T11:29:23.371590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2704388
42.5%
10000000 615
 
< 0.1%
20000000 219
 
< 0.1%
30000000 86
 
< 0.1%
40000000 31
 
< 0.1%
102 21
 
< 0.1%
198 19
 
< 0.1%
125 18
 
< 0.1%
160 18
 
< 0.1%
132 18
 
< 0.1%
Other values (3614687) 3657187
57.5%
ValueCountFrequency (%)
0 2704388
42.5%
0.01 1
 
< 0.1%
0.03 1
 
< 0.1%
0.13 1
 
< 0.1%
0.33 1
 
< 0.1%
0.37 1
 
< 0.1%
0.79 1
 
< 0.1%
1 7
 
< 0.1%
1.39 1
 
< 0.1%
1.64 1
 
< 0.1%
ValueCountFrequency (%)
356015889.4 1
< 0.1%
355553416.3 1
< 0.1%
355381433.6 1
< 0.1%
355380483.5 1
< 0.1%
355185537.1 1
< 0.1%
328194464.9 1
< 0.1%
327998074.2 1
< 0.1%
327963024 1
< 0.1%
327852121.4 1
< 0.1%
327827763.4 1
< 0.1%

newbalanceDest
Real number (ℝ)

High correlation  Zeros 

Distinct3555499
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1224996.4
Minimum0
Maximum3.5617928 × 108
Zeros2439433
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size48.5 MiB
2025-08-31T11:29:23.600293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median214661.44
Q31111909.2
95-th percentile5515715.9
Maximum3.5617928 × 108
Range3.5617928 × 108
Interquartile range (IQR)1111909.2

Descriptive statistics

Standard deviation3674128.9
Coefficient of variation (CV)2.9992978
Kurtosis862.15651
Mean1224996.4
Median Absolute Deviation (MAD)214661.44
Skewness19.352302
Sum7.7941866 × 1012
Variance1.3499223 × 1013
MonotonicityNot monotonic
2025-08-31T11:29:23.830258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2439433
38.3%
10000000 53
 
< 0.1%
971418.91 32
 
< 0.1%
19169204.93 29
 
< 0.1%
16532032.16 25
 
< 0.1%
1254956.07 25
 
< 0.1%
1412484.09 22
 
< 0.1%
4743010.67 21
 
< 0.1%
1178808.14 21
 
< 0.1%
7364724.84 21
 
< 0.1%
Other values (3555489) 3922938
61.7%
ValueCountFrequency (%)
0 2439433
38.3%
0.01 1
 
< 0.1%
0.33 1
 
< 0.1%
1.39 1
 
< 0.1%
1.64 1
 
< 0.1%
1.74 1
 
< 0.1%
2.15 1
 
< 0.1%
2.45 1
 
< 0.1%
2.71 1
 
< 0.1%
2.76 1
 
< 0.1%
ValueCountFrequency (%)
356179278.9 1
< 0.1%
356015889.4 1
< 0.1%
355553416.3 2
< 0.1%
355381433.6 1
< 0.1%
355380483.5 1
< 0.1%
355185537.1 1
< 0.1%
328431698.2 1
< 0.1%
328194464.9 1
< 0.1%
327998074.2 1
< 0.1%
327963024 1
< 0.1%

isFraud
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.5 MiB
0
6354407 
1
 
8213

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6362620
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 6354407
99.9%
1 8213
 
0.1%

Length

2025-08-31T11:29:24.021527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-31T11:29:24.133385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6354407
99.9%
1 8213
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 6354407
99.9%
1 8213
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6354407
99.9%
1 8213
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6354407
99.9%
1 8213
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6354407
99.9%
1 8213
 
0.1%

isFlaggedFraud
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.5 MiB
0
6362604 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6362620
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6362604
> 99.9%
1 16
 
< 0.1%

Length

2025-08-31T11:29:24.264472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-31T11:29:24.370028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6362604
> 99.9%
1 16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 6362604
> 99.9%
1 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6362604
> 99.9%
1 16
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6362604
> 99.9%
1 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6362604
> 99.9%
1 16
 
< 0.1%

Interactions

2025-08-31T11:28:28.868326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:27:59.557596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:05.925165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:11.278413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:17.607056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:23.018362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:30.089302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:00.624427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:06.793056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:12.189436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:18.524896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:23.952548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:31.224766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:01.817123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:07.669424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:13.046692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:19.442558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:24.859331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:32.133297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:03.000232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:08.570255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:14.067900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:20.291631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:25.782230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:33.050606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:04.088409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:09.468166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:15.270571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:21.195545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:26.647649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:33.928476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:05.008119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:10.374254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:16.465811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:22.101177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T11:28:27.613623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-31T11:29:24.468617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
amountisFlaggedFraudisFraudnewbalanceDestnewbalanceOrigoldbalanceDestoldbalanceOrgsteptype
amount1.0000.0140.0490.670-0.0710.5950.0480.0010.050
isFlaggedFraud0.0141.0000.0430.0000.0050.0000.0030.0060.005
isFraud0.0490.0431.0000.0020.0190.0020.0310.0590.059
newbalanceDest0.6700.0000.0021.000-0.0940.936-0.008-0.0050.027
newbalanceOrig-0.0710.0050.019-0.0941.0000.0440.803-0.0110.238
oldbalanceDest0.5950.0000.0020.9360.0441.0000.024-0.0050.017
oldbalanceOrg0.0480.0030.031-0.0080.8030.0241.000-0.0060.213
step0.0010.0060.059-0.005-0.011-0.005-0.0061.0000.011
type0.0500.0050.0590.0270.2380.0170.2130.0111.000

Missing values

2025-08-31T11:28:35.153338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-31T11:28:40.113147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

steptypeamountnameOrigoldbalanceOrgnewbalanceOrignameDestoldbalanceDestnewbalanceDestisFraudisFlaggedFraud
01PAYMENT9839.64C1231006815170136.00160296.36M19797871550.00.0000
11PAYMENT1864.28C166654429521249.0019384.72M20442822250.00.0000
21TRANSFER181.00C1305486145181.000.00C5532640650.00.0010
31CASH_OUT181.00C840083671181.000.00C3899701021182.00.0010
41PAYMENT11668.14C204853772041554.0029885.86M12307017030.00.0000
51PAYMENT7817.71C9004563853860.0046042.29M5734872740.00.0000
61PAYMENT7107.77C154988899183195.00176087.23M4080691190.00.0000
71PAYMENT7861.64C1912850431176087.23168225.59M6333263330.00.0000
81PAYMENT4024.36C12650129282671.000.00M11769321040.00.0000
91DEBIT5337.77C71241012441720.0036382.23C19560086041898.040348.7900
steptypeamountnameOrigoldbalanceOrgnewbalanceOrignameDestoldbalanceDestnewbalanceDestisFraudisFlaggedFraud
6362610742TRANSFER63416.99C77807100863416.990.0C18125528600.000.0010
6362611742CASH_OUT63416.99C99495068463416.990.0C1662241365276433.18339850.1710
6362612743TRANSFER1258818.82C15313014701258818.820.0C14709985630.000.0010
6362613743CASH_OUT1258818.82C14361187061258818.820.0C1240760502503464.501762283.3310
6362614743TRANSFER339682.13C2013999242339682.130.0C18504239040.000.0010
6362615743CASH_OUT339682.13C786484425339682.130.0C7769192900.00339682.1310
6362616743TRANSFER6311409.28C15290082456311409.280.0C18818418310.000.0010
6362617743CASH_OUT6311409.28C11629223336311409.280.0C136512589068488.846379898.1110
6362618743TRANSFER850002.52C1685995037850002.520.0C20803885130.000.0010
6362619743CASH_OUT850002.52C1280323807850002.520.0C8732211896510099.117360101.6310